Libraries tagged by Pollution

A trait that provides a convenient solution for objects to use an array as their data source. As well as offering the possibility to share the same data between instances.

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Modern solution for easily building strictly-typed data objects and entities for tests

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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Nova Nested Fields is a Laravel Nova field package that allows users to present their checkboxes or radio buttons in a nested, hierarchical structure. This package supports infinite nesting levels, providing a flexible solution for complex form requirements.

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QStash SDK for PHP - QStash is an HTTP based messaging and scheduling solution for serverless and edge runtimes

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Very easy and powerful socket server solution with describing of any protocol and client scenario. Based on ReactPHP/socket

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Php sdk of Feexpay - Online payment solution by credit card and mobile money"

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Fully integrated Backup solution for Craft CMS

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Message Queue solutions for Magento2. Supports RabbitMQ, AMQP, STOMP, Amazon SQS, Kafka, Redis, Google PubSub, Gearman, Beanstalk, Google PubSub

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A PHP library that connects to the Backstop Solutions API.

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🚀 Enterprise-ready Laravel SMS package supporting 8+ providers including Bangladeshi (DhorolaSMS, BulkSMSBD, MimSMS, eSMS, REVE SMS, Alpha SMS) & international gateways (Twilio, Nexmo). Features automatic fallback, queue integration, activity tracking, balance checking, and fluent API for production-grade SMS solutions.

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full solutions making yii2-framework run on swoole with coroutine

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Generic monitoring solution for web applications

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Channelize.io live Shopping can help in transforming online sale with complete live stream shopping solution.

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Carts Guru - Multichannel solution to convert abandoned shopping carts into sales.

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